Modeling sparse connectivity between underlying brain sources for EEG/MEG

We propose a novel technique to assess functional brain connectivity in EEG/MEG signals. Our method, called Sparsely-Connected Sources Analysis (SCSA), can overcome the problem of volume conduction by modeling neural data innovatively with the following ingredients: (a) the EEG is assumed to be a linear mixture of correlated sources following a multivariate autoregressive (MVAR) model, (b) the demixing is estimated jointly with the source MVAR parameters, (c) overfitting is avoided by using the Group Lasso penalty. This approach allows to extract the appropriate level cross-talk between the extracted sources and in this manner we obtain a sparse data-driven model of functional connectivity. We demonstrate the usefulness of SCSA with simulated data, and compare to a number of existing algorithms with excellent results.Comment: 9 pages, 6 figure

Evaluating functional connectivity in alcoholics based on maximal weight matching

EEG-based applications have faced the challenge of multi-modal integrated analysis problems. In this paper, a greedy maximal weight matching approach is used to measure the functional connectivity in alcoholics datasets with EEG and EOG signals. The major discovery is that the processing of the repeated and unrepeated stimuli in the γ band in control drinkers is significantly more different than that in alcoholic subjects. However, the EOGs are always stable in the case of visual tasks, except for a weakly wave when subjects make an error response to the stimul

Fast non-negative deconvolution for spike train inference from population calcium imaging

Calcium imaging for observing spiking activity from large populations of neurons are quickly gaining popularity. While the raw data are fluorescence movies, the underlying spike trains are of interest. This work presents a fast non-negative deconvolution filter to infer the approximately most likely spike train for each neuron, given the fluorescence observations. This algorithm outperforms optimal linear deconvolution (Wiener filtering) on both simulated and biological data. The performance gains come from restricting the inferred spike trains to be positive (using an interior-point method), unlike the Wiener filter. The algorithm is fast enough that even when imaging over 100 neurons, inference can be performed on the set of all observed traces faster than real-time. Performing optimal spatial filtering on the images further refines the estimates. Importantly, all the parameters required to perform the inference can be estimated using only the fluorescence data, obviating the need to perform joint electrophysiological and imaging calibration experiments.Comment: 22 pages, 10 figure

Color screening in a constituent quark model of hadronic matter

The effect of color screening on the formation of a heavy quark-antiquark ($Q\bar{Q}$) bound state--such as the $J/\psi$ meson--is studied using a constituent-quark model. The response of the nuclear medium to the addition of two color charges is simulated directly in terms of its quark constituents via a string-flip potential that allows for quark confinement within hadrons yet enables the hadrons to separate without generating unphysical long-range forces. Medium modifications to the properties of the heavy meson, such as its energy and its mean-square radius, are extracted by solving Schr\"odinger's equation for the $Q\bar{Q}$ pair in the presence of a (screened) density-dependent potential. The density dependence of the heavy-quark potential is in qualitative agreement with earlier studies of its temperature dependence extracted from lattice calculations at finite temperature. In the present model it is confirmed that abrupt changes in the properties of the $J/\psi$-meson in the hadronic medium ({\it plasma}), correlate strongly with the deconfining phase transition.Comment: 7 pages, 3 figures, submitted to PRC for publication, uses revtex

Methods, systems, and devices for pairing vagus nerve stimulation with motor therapy in stroke patients

A method of treating motor deficits in a stroke patient, comprising assessing a patient's motor deficits, determining therapeutic goals for the patient, based on the patient's motor deficits, selecting therapeutic tasks based on the therapeutic goals, performing each of the selected therapeutic tasks repetitively, observing the performance of the therapeutic tasks, initiating the stimulation of the vagus nerve manually at approximately a predetermined moment during the performance of the therapeutic tasks, stimulating the vagus nerve of the patient during the performance of the selected therapeutic tasks, and improving the patient's motor deficits.Board of Regents, University of Texas Syste

Enhancing the Performance of Single-Channel Blind Source Separation by Using ConvTransFormer

In the specialized field of audio signal processing, this study introduces a pioneering ConvTransFormer architecture aimed at enhancing the performance of single-channel blind source separation (SCBSS). This innovative architecture ingeniously combines the strengths of a multiple simple-weak attention mechanism with the triple-gating feature of a Gated Attention Unit (GAU) within the ConvTransFormer. This combination allows for a more focused and effective targeting of specific segments within the input sequence. The efficacy of this ConvTransFormer architecture is rigorously evaluated using the WSJ0-2mix dataset, a standard benchmark in the field. The results of this evaluation are significant, demonstrating substantial improvements in key performance metrics. Notably, there is an increase in the Signal-to-Interference (SI)-Signal-to-Noise Ratio improvement (SNRi) by 16.5 and in the Signal-to-Distortion Ratio improvement (SDRi)-Signal-to-Interference (SDRi) by 16.8. These improvements are crucial indicators of the quality of source separation in SCBSS. The findings of this research are groundbreaking, indicating that the proposed ConvTransFormer architecture surpasses existing methods in both SI-SNRi and SDRi performance metrics. This advancement marks a significant step forward in the field of SCBSS, offering new avenues for more effective and precise audio signal processing, especially in scenarios where isolating individual sound sources from a single- channel input is essential